class_docstr: Conditional Random Field layer (tf.keras)
  `CRF` can be used as the last layer in a network (as a classifier). Input shape (features)
  must be equal to the number of classes the CRF can predict (a linear layer is recommended).
  
  Note: the loss and accuracy functions of networks using `CRF` must
  use the provided loss and accuracy functions (denoted as loss and viterbi_accuracy)
  as the classification of sequences are used with the layers internal weights.
  
  Args:
      num_labels (int): the number of labels to tag each temporal input.
  
  Input shape:
      nD tensor with shape `(batch_size, sentence length, num_classes)`.
  
  Output shape:
      nD tensor with shape: `(batch_size, sentence length, num_classes)`.
code: def get_config(self):
      config = {
          "output_dim": self.output_dim,
          "supports_masking": self.supports_masking,
          "transitions": tf.keras.backend.eval(self.transitions),
      }
      base_config = super(CRF, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
file_path: nlp-architect/nlp_architect/nn/tensorflow/python/keras/layers/crf.py
imports: import tensorflow
label_desc: Use image node_id 2 for calling the CRF obj's underlying member method code with example usage: obj.get_config() and returns: dict